Communication Dans Un Congrès Année : 2023

AJAR: An Argumentation-based Judging Agents Framework for Ethical Reinforcement Learning

Résumé

An increasing number of socio-technical systems embedding Artificial Intelligence (AI) technologies are deployed, and questions arise about the possible impact of such systems onto humans. We propose a hybrid multi-agent Reinforcement Learning framework consists of learning agents that learn a task-oriented behaviour defined by a set of symbolic moral judging agents to ensure they respect moral values. This framework is applied on the problem of responsible energy distribution for smart grids.

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Cite 10.5281/zenodo.7628903 Ouvrage Alcaraz, B., Boissier, O., Chaput, R., & Leturc, C. (2023). AJAR: An Argumentation-based Judging Agents Framework for Ethical Reinforcement Learning (Version v1). Zenodo. https://doi.org/10.5281/ZENODO.7628903

Dates et versions

hal-04127943 , version 1 (14-06-2023)

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Benoît Alcaraz, Olivier Boissier, Rémy Chaput, Christopher Leturc. AJAR: An Argumentation-based Judging Agents Framework for Ethical Reinforcement Learning. AAMAS '23: International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, May 2023, London, United Kingdom. ⟨10.5555/3545946.3598956⟩. ⟨hal-04127943⟩
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